ML Practice Leader (Team Lead) (copy)

Provectus; Armenia; Presencial; COP 74.117.000 - 111.177.000; Jornada completa

Descripción de la vacante

A technology consulting firm in Quindío, Armenia is seeking a Practice Leader to manage teams and drive ML projects. The ideal candidate will have extensive experience in ML best practices, strong leadership skills, and a robust understanding of the ML project lifecycle, including data monitoring and automation. This is an intermediate-level position offering opportunities for growth and mentorship within a dynamic team environment.

Formación

• Practical experience in Python and ML best practices.
• Ability to explain technical concepts to non-technical stakeholders.
• Team leadership and mentoring experience.

Responsabilidades

• Build effective teams.
• Mentor engineers and coach Team Leads.
• Conduct performance reviews and 1-on-1 meetings.
• Participate in community events.

Conocimientos

Python patterns & best practices

ML project lifecycle

Deep Learning models

NLP, CV, forecasting, recommender systems

agentic workflows, RAG architecture, GraphRAG

transformers

MLOps platforms

data monitoring, retraining automation

ML pipelines components

DataOps or ML/MLOps

mentor skills

diplomatic skills

communication skills

conflict resolution

Descripción del empleo

A Practice Leader is the first point of contact for their direct reports, and they liaise and relay information between senior leaders, HR, and engineers.

The role of a Practice Leader is probably the most important because this is the first line of people management, and it provides things to be done. On the one hand, a PL is a part of the Practice, so this person must be at least senior‑level in their specialty. On the other hand, a Practice Leader is a people manager.

Requirements

• Practical experience and strong understanding of Python patterns & best practices
• Strong understanding of ML project lifecycle
• Practical experience with creating training datasets involving human annotators
• Experience with writing Deep Learning models from scratch
• Experience in >1 of the following areas: NLP, CV, forecasting, recommender systems
• Strong experience with agentic workflows, RAG architecture, and GraphRAG
• Experience and in‑depth understanding of transformers
• Practical experience with /AWS/other cloud/open source alternatives/ MLOps platforms, frameworks, and libraries
• Practical experience with model post‑production & maintenance: model and data monitoring, retraining automation, etc.
• Ability to make reusable components of ML pipelines
• Practical experience with a variety of data sources (OLTP, OLAP, DataLake, Streaming)
• Experience in DataOps or ML/MLOps would be a significant plus
• Ability to explain decisions, status, and roadmap to non‑technical customer representatives
• Experience in team/department leadership
• Ability to teach and mentor. The role assumes providing employees with their career path and helping them achieve goals
• Diplomatic skills. It means more than just "communication skills" and includes ethics, empathy, compassion, and the ability to resolve conflicts
• Calmness. People are complicated, and you need to be ready for any objectives or misunderstandings

Responsibilities

• Build effective teams
• Participate in meetups, conferences, and build community
• Share best practices and culture with the team
• Mentor engineers, coach Team Leads, and encourage others to share knowledge
• Have technical excellence and be an influencer in different teams/projects
• Hire and onboard newcomers
• Conduct performance reviews, 1‑on‑1 meetings
• Identify and address team gaps in knowledge
• Evaluate, improve, and maintain processes
• Collaborate with other managers across the company
• Communicate and follow the company's mission, vision, and values

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

Nivel de antigüedad

Intermedio